![]() īased on this understanding, the recognition of lung sound patterns using machine learning has been achieved, providing an objective and quantitative method for lung health assessment. The emergence of digital stethoscopes combined with related physics study has contributed to our understanding of lung sounds including, their production, transmission, and characteristics under healthy and pathological conditions. ![]() In addition, wireless transmission (e.g., Bluetooth or WiFi) allows it to be used for remote diagnosis, further increasing the convenience of application. It enables the visualization and retrospective analysis of lung sounds. To this end, the digital stethoscope has been developed to record lung sounds by digitizing acoustic signals. These challenges need to be resolved to improve the quality and efficiency of lung disease diagnosis. The subjectivity of the diagnosis is further amplified by the lack of a recording function in the conventional stethoscope that prevents other personnel from analyzing the sounds heard during the consultation. Second, the medical-decisions made based on auscultation are subject to inter-listener variability in proficiency. First, the interpretation of lung sounds requires a trained paramedic, limiting stethoscope use in low-resource areas. In contrast, auscultation offers a non-invasive, low-cost, and portable way of working where paramedics use a conventional acoustic stethoscope to diagnose lung diseases, including asthma, chronic obstructive pulmonary disease (COPD), and pneumonia, based on the patient's lung sound.Īlthough the stethoscope has been widely used in clinics, it has several associated challenges. However, these methods are often limited to high-end clinics due to their complexity and high costs. Various clinical methods have been developed to diagnose and evaluate lung health conditions, including computed tomographic scans, chest X-rays, and pulmonary function tests (PFTs). Lung disease has been a leading cause of mortality worldwide for many years, especially since the onset of corona virus disease 2019 (COVID-19). ![]() To address the poor reproducibility and variety of deep learning in this field, this review also provides a scalable and flexible open-source framework that aims to standardize the algorithmic workflow and provide a solid basis for replication and future extension. Additionally, this review highlights current challenges in this field, including the variety of devices, noise sensitivity, and poor interpretability of deep models. We focus on each component of deep learning-based lung sound analysis systems, including the task categories, public datasets, denoising methods, and, most importantly, existing deep learning methods, i.e., the state-of-the-art approaches to convert lung sounds into two-dimensional (2D) spectrograms and use convolutional neural networks for the end-to-end recognition of respiratory diseases or abnormal lung sounds. ![]() This review thus aims to provide a comprehensive overview of deep learning algorithms used for lung sound analysis to emphasize the significance of artificial intelligence (AI) in this field. On this basis, machine learning, particularly deep learning, enables the fully-automatic analysis of lung sounds that may pave the way for intelligent stethoscopes. The emergence of digital stethoscopes has overcome these limitations by allowing physicians to store and share respiratory sounds for consultation and education. However, traditional stethoscopes have inherent limitations, such as inter-listener variability and subjectivity, and they cannot record respiratory sounds for offline/retrospective diagnosis or remote prescriptions in telemedicine. Auscultation is crucial for the diagnosis of respiratory system diseases.
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